Investigating the global prevalence and consequences of undiagnosed stage 3 chronic kidney disease: methods and rationale for the REVEAL-CKD study
Bibliographic record
Abstract
Background: Timely diagnosis and treatment of stage 3 chronic kidney disease (CKD) can prevent further loss of kidney function and progression to kidney failure. However, contemporary data on the global prevalence of undiagnosed stage 3 CKD are scarce. REVEAL-CKD is a multinational, multifocal and observational study aiming to provide insights into undiagnosed stage 3 CKD in a large population. Methods: , recorded >90 and ≤730 days apart. Undiagnosed cases are those without an International Classification of Diseases 9/10 diagnosis code for CKD (any stage) any time before and up to 6 months after the second qualifying eGFR measurement. Time to diagnosis will be assessed using a Kaplan-Meier approach; patient characteristics associated with undiagnosed CKD will be assessed using adjusted logistical regression analyses. Results: REVEAL-CKD will assess the point prevalence of undiagnosed stage 3 CKD and time to CKD diagnosis in initially undiagnosed cases overall and in individual countries. Trends in undiagnosed CKD prevalence by calendar year will be assessed. Patient characteristics, healthcare resource utilization, adverse clinical outcomes, and CKD management and monitoring practices in patients with versus without a CKD diagnosis will be compared. Conclusions: REVEAL-CKD will increase awareness of the global clinical and economic burden of undiagnosed stage 3 CKD and provide valuable insights to inform clinical practice and policy changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".